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⇤ ← Revision 1 as of 2007-02-12 12:12:42
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| Suppose a group of controls and a single case do a pair of conditions and we are interested in seeing if the relationship between the conditions in the controls is the same as that in the single case. | Suppose a group of controls and a single case do a pair of conditions (c1, c2, say) and we are interested in seeing if the relationship between the conditions in the controls is the same as that in the single case. |
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| There is a one-sample t-test option under analyze:means in SPSS. Alternatively the below example will work using R. | There is a one-sample t-test option under analyze:means in SPSS. Alternatively the below example will work using R. Here c1 and c2 are the two conditions and sub denotes if the subject is a control or a case. |
== Comparing a pairwise mean difference to a single case ==
Suppose a group of controls and a single case do a pair of conditions (c1, c2, say) and we are interested in seeing if the relationship between the conditions in the controls is the same as that in the single case.
Since we have a single case we can treat its difference as a constant. So we end up with a one sample t-test on the control group difference minus the case difference. Ie we test if the mean difference in controls - difference in case equals 0.
There is a one-sample t-test option under analyze:means in SPSS. Alternatively the below example will work using R. Here c1 and c2 are the two conditions and sub denotes if the subject is a control or a case.
c1 <- c(2,1,2,3,1,2,2,1,1,3,3,1) c2 <- c(3,4,1,1,6,1,3,2,2,5,2,2) sub <- c(1,1,1,1,1,1,1,1,1,1,1,2) diff <- c1 – c2 diffn <- diff[sub == 1] diffp <- diff[sub == 2] diffy <- diffn - diffp const <- gl(1,length(diffn)) id <- gl(length(diffn),1) t.test(diffy, mu=0)